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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 11311140 of 9051 papers

TitleStatusHype
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion GenerationCode1
GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive TestingCode1
G-Eval: NLG Evaluation using GPT-4 with Better Human AlignmentCode1
GPT-FL: Generative Pre-trained Model-Assisted Federated LearningCode1
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
Adaptively Sparse TransformersCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data FusionCode1
Forecasting Future World Events with Neural NetworksCode1
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